Data Science Foundations: Data Mining

Introduction

• Welcome
• Who should watch this course
• Exercise files

1. Preliminaries

• Data mining prerequisites
• Algorithm prerequisites
• Software prerequisites

2. Data Reduction

• Goals of data reduction
• Data for data reduction
• Data reduction in R
• Data reduction in Python
• Data reduction in Orange
• Data reduction in RapidMiner

3. Clustering

• Clustering goals
• Clustering data
• Clustering in R
• Clustering in Python
• Clustering in BigML
• Clustering in Orange

4. Classification

• Classification goals
• Classification data
• Classification in R
• Classification in Python
• Classification in RapidMiner
• Classification in KNIME

5. Anomaly Detection

• Anomaly detection goals
• Anomaly detection data
• Anomaly detection in R
• Anomaly detection in Python
• Anomaly detection in BigML
• Anomaly detection in RapidMiner

6. Association Analysis

• Association analysis goals
• Association analysis data
• Association analysis in R
• Association analysis in Python
• Association analysis in Orange
• Association analysis in RapidMiner

7. Regression Analysis

• Regression analysis goals
• Regression analysis data
• Regression analysis in R
• Regression analysis in Python
• Regression analysis in KNIME
• Regression analysis in RapidMiner

8. Sequential Patterns

• Sequence mining goals
• Sequence mining algorithms
• Sequence mining in R
• Sequence mining in Python
• Sequence mining in BigML: Part 1
• Sequence mining in BigML: Part 2

9. Text Mining

• Text mining goals
• Text mining algorithms
• Text mining in R
• Text mining in Python
• Text mining in RapidMiner

Conclusion

• Next steps